class_docstr: Base class for training strategies.
code: def setup_sampler(self, dataset) -> DistributedSampler:
      # FIXME(cwher): this is only invoked in train_on_ray, not tested after adapt Boost API.
      return DistributedSampler(dataset, 1, 0)
  
imports: from abc import ABC, abstractmethod
  from contextlib import nullcontext
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from coati.experience_buffer import ExperienceBuffer
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from colossalai.booster import Booster
  from colossalai.booster.plugin import Plugin
  from .sampler import DistributedSampler
label_desc: Use image node_id 14 for calling the Strategy obj's underlying member method code with example usage: obj.setup_sampler(dataset) and returns: DistributedSampler